Significant progress has been made on physics-based torque-and-drag (T&D) models that can run either offline or in real time. Despite its numerous benefits, real-time T&D analysis is not prevalent because it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration, the results of which are not easily accessible to the user. In this paper, the application of a real-time T&D model is demonstrated. The process of T&D analysis was automated, and the time and cost required to run physical models offline was reduced or, in some cases, eliminated.

Introduction

Traditional electronic drilling recorders (EDRs) are third-party systems that collect rig-sensor data. Major limitations in the operator’s ability to fully leverage the potential of this data exist, including issues with rig-sensor-measurement quality and rigsite data-aggregation methods, relatively slow data-sampling rates, and limited interoperability. To this end, the operator initiated a project to use drilling data better, focusing on improving data quality by building on previous work validating rig-sensor data.

Rig-Based T&D Advisory System

T&D Modeling. Onshore US operators are investing heavily in unconventional horizontal plays. In these wells, excessive T&D is a critical limiting factor in exposure to productive formations.

Predictive T&D computer models were developed as early as 1984. An initial model assumed that T&D are both caused by sliding friction on the wellbore, using the product of the normal force and friction coefficient to yield values for T&D. Later, the model was put in standard differential form and made to include the effects of mud pressure. These models (now termed soft-string) treat tubulars like a rope, ignoring bending moments and assuming continuous contact with the wellbore.

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